| Title: | Analysing Conditional Income Distributions |
|---|---|
| Description: | Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level. |
| Authors: | Alexander Sohn |
| Maintainer: | Alexander Sohn <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1 |
| Built: | 2024-12-01 08:04:22 UTC |
| Source: | CRAN |
Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.
| Package: | acid |
| Type: | Package |
| Version: | 1.1 |
| Date: | 2015-01-06 |
| License: | GPL-3 |
sadr.test,
polarisation.ER,
gini.den
Alexander Sohn <[email protected]>
Klein, N. and Kneib, T., Lang, S. and Sohn, A. (2015): Bayesian Structured Additive Distributional Regression with an Application to Regional Income Inequality in Germany, in: Annals of Applied Statistics, Vol. 9(2), pp. 1024-1052.
Sohn, A., Klein, N. and Kneib. T. (2014): A New Semiparametric Approach to Analysing Conditional Income Distributions, in: SOEPpapers, No. 676.
This function calculates the expectation of the Generalised Beta Distribution of Second Kind.
arithmean.GB2(b, a, p, q)arithmean.GB2(b, a, p, q)
b |
the parameter b of the Dagum distribution as defined by Kleiber and Kotz (2003). |
a |
the parameter a of the Dagum distribution as defined by Kleiber and Kotz (2003). |
p |
the parameter p of the Dagum distribution as defined by Kleiber and Kotz (2003). |
q |
the parameter q of the Dagum distribution as defined by Kleiber and Kotz (2003). |
returns the expectation.
Alexander Sohn
Kleiber, C. and Kotz, S. (2003): Statistical Size Distributions in Economics and Actuarial Sciences, Wiley, Hoboken.
a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,q.test) arithmean.GB2(b.test,a.test,p.test,q.test) mean(GB2sample)a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,q.test) arithmean.GB2(b.test,a.test,p.test,q.test) mean(GB2sample)
This function computes the Atkinson inequality index for a vector of observations.
atkinson(x, epsilon = 1)atkinson(x, epsilon = 1)
x |
a vector of observations. |
epsilon |
inequality aversion parameter as denoted by Atkinson (1970). The default is epsilon=1. |
returns the selected Atkinson inequality index.
Alexander Sohn
Atkinson, A.B. (1970): On the Measurment of Inequality, in: Journal of Economic Theory, Vol. 2(3), pp. 244-263.
x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) atkinson(x)x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) atkinson(x)
This function approximates the Atkinson index for a distribution specified by a vector of densities and a corresponding income vector. A point mass at zero is allowed.
atkinson.den(incs, dens, epsilon = 1, pm0 = NA, lower = NULL, upper = NULL, zero.approx = NULL)atkinson.den(incs, dens, epsilon = 1, pm0 = NA, lower = NULL, upper = NULL, zero.approx = NULL)
incs |
a vector with income values. |
dens |
a vector with the corresponding densities. |
epsilon |
inequality aversion parameter as denoted by Atkinson (1970). The default is epsilon=1. |
pm0 |
the point mass for zero incomes. If not specified no point mass is assumed. |
lower |
the lower bound of the income range considered. |
upper |
the upper bound of the income range considered. |
zero.approx |
a scalar which replaces zero-incomes, such that the Atkinson index involving a logarithm return finite values. |
AIM |
the approximation of the selected Atkinson inequality measure. |
epsilon |
the inequality aversion parameter used. |
mean |
the approximated expected value of the distribution. |
pm0 |
the point mass for zero incomes used. |
lower |
the lower bound of the income range considered used. |
upper |
the upper bound of the income range considered used. |
zero.approx |
the zero approximation used. |
Alexander Sohn
Atkinson, A.B. (1970): On the Measurment of Inequality, in: Journal of Economic Theory, Vol. 2(3), pp. 244-263.
## without point mass at zero incs<-seq(0,500,by=0.01) dens<-dLOGNO(incs,2,1) plot(incs,dens,type="l",xlim=c(0,100)) atkinson.den(incs=incs,dens=dens,epsilon=1)$AIM atkinson(rLOGNO(50000,2,1),epsilon=1) atkinson.den(incs=incs,dens=dens,epsilon=0.5)$AIM atkinson(rLOGNO(50000,2,1),epsilon=0.5) ## with point mass at zero incs<-c(seq(0,100,by=0.1),seq(100.1,1000,by=1),seq(1001,10000,by=10)) dens<-dLOGNO(incs,2,1)/2 dens[1]<-0.5 plot(incs,dens,type="l",ylim=c(0,max(dens[-1])),xlim=c(0,100)) #without zero approx zeros atkinson.den(incs=incs,dens=dens,epsilon=1,pm0=0.5)$AIM atkinson(c(rep(0,25000),rLOGNO(25000,2,1)),epsilon=1) atkinson.den(incs=incs,dens=dens,epsilon=0.5,pm0=0.5)$AIM atkinson(c(rep(0,25000),rLOGNO(25000,2,1)),epsilon=0.5) #with zero approximation atkinson.den(incs=incs,dens=dens,epsilon=0.5,pm0=0.5,zero.approx=1)$AIM atkinson(c(rep(1,25000),rLOGNO(25000,2,1)),epsilon=0.5) atkinson.den(incs=incs,dens=dens,epsilon=1,pm0=0.5,zero.approx=0.01)$AIM atkinson(c(rep(0.01,250000),rLOGNO(250000,2,1)),epsilon=1)## without point mass at zero incs<-seq(0,500,by=0.01) dens<-dLOGNO(incs,2,1) plot(incs,dens,type="l",xlim=c(0,100)) atkinson.den(incs=incs,dens=dens,epsilon=1)$AIM atkinson(rLOGNO(50000,2,1),epsilon=1) atkinson.den(incs=incs,dens=dens,epsilon=0.5)$AIM atkinson(rLOGNO(50000,2,1),epsilon=0.5) ## with point mass at zero incs<-c(seq(0,100,by=0.1),seq(100.1,1000,by=1),seq(1001,10000,by=10)) dens<-dLOGNO(incs,2,1)/2 dens[1]<-0.5 plot(incs,dens,type="l",ylim=c(0,max(dens[-1])),xlim=c(0,100)) #without zero approx zeros atkinson.den(incs=incs,dens=dens,epsilon=1,pm0=0.5)$AIM atkinson(c(rep(0,25000),rLOGNO(25000,2,1)),epsilon=1) atkinson.den(incs=incs,dens=dens,epsilon=0.5,pm0=0.5)$AIM atkinson(c(rep(0,25000),rLOGNO(25000,2,1)),epsilon=0.5) #with zero approximation atkinson.den(incs=incs,dens=dens,epsilon=0.5,pm0=0.5,zero.approx=1)$AIM atkinson(c(rep(1,25000),rLOGNO(25000,2,1)),epsilon=0.5) atkinson.den(incs=incs,dens=dens,epsilon=1,pm0=0.5,zero.approx=0.01)$AIM atkinson(c(rep(0.01,250000),rLOGNO(250000,2,1)),epsilon=1)
This function computes the Atkinson index (I(epsilon)) for Generalised Beta Distribution of Second Kind. The function is exact for the values epsilon=0, epsilon=1 and epsilon=2. For other values of epsilon, the function provides a numerical approximation.
atkinson.GB2(b, a, p, q, epsilon = NULL, ylim = c(0, 1e+06), zeroapprox = 0.01)atkinson.GB2(b, a, p, q, epsilon = NULL, ylim = c(0, 1e+06), zeroapprox = 0.01)
b |
the parameter b of the Dagum distribution as defined by Kleiber and Kotz (2003). |
a |
the parameter a of the Dagum distribution as defined by Kleiber and Kotz (2003). |
p |
the parameter p of the Dagum distribution as defined by Kleiber and Kotz (2003). |
q |
the parameter q of the Dagum distribution as defined by Kleiber and Kotz (2003). |
epsilon |
inequality aversion parameter as denoted by Atkinson (1970). The default is epsilon=1. |
ylim |
limits of the interval of y considered needed for the approximation of the entropy measure. The default is [0,1e+06]. |
zeroapprox |
an approximation for zero needed for the approximation of the entropy measure. The default is 0.01. |
returns the selected Atkinson inequality index.
Alexander Sohn
Atkinson, A.B. (1970): On the Measurment of Inequality, in: Journal of Economic Theory, Vol. 2(3), pp. 244-263.
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 87-166, Elsevier, Amsterdam.
a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 epsilon.test<-1 GB2sample<-rGB2(1000,b.test,a.test,p.test,q.test) atkinson.GB2(b.test,a.test,p.test,q.test,epsilon=epsilon.test,ylim=c(0,1e+07)) atkinson(GB2sample, epsilon.test)a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 epsilon.test<-1 GB2sample<-rGB2(1000,b.test,a.test,p.test,q.test) atkinson.GB2(b.test,a.test,p.test,q.test,epsilon=epsilon.test,ylim=c(0,1e+07)) atkinson(GB2sample, epsilon.test)
This function uses Monte Carlo methods to estimate the Atkinson index for a mixture of two continuous income distributions and a point mass for zero-incomes.
atkinson.md(n, epsilon = 1, dist1, dist2, theta, p0, p1, p2, dist.para.table, zero.approx)atkinson.md(n, epsilon = 1, dist1, dist2, theta, p0, p1, p2, dist.para.table, zero.approx)
n |
sample size used to estimate the Atkinson index. |
epsilon |
inequality aversion parameter as denoted by Atkinson (1970). The default is epsilon=1. |
dist1 |
character string with the name of the first continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
dist2 |
character string with the name of the second continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
theta |
vector with the parameters of dist1 and dist2. Order must be the same as in the functions for the distributions. |
p0 |
scalar with probability mass for the point mass. |
p1 |
scalar with probability mass for dist1. |
p2 |
scalar with probability mass for dist2. |
dist.para.table |
a table of the same form as |
zero.approx |
a scalar which replaces zero-incomes, such that the Atkinson index involving a logarithm return finite values. |
AIM |
the selected Atkinson inequality measure. |
epsilon |
the inequality aversion parameter used. |
y |
a vector with the simulated incomes to estimate the entropy measure. |
y2 |
a vector with the zero-replaced simulated incomes to estimate the entropy measure. |
zero.replace |
a logical vector indicating whether a zero has been replaced. |
stat |
a vector with the simulated group the observation was chosen from. 0 is the point mass, 1 dist1 and 2 dist2. |
Alexander Sohn
Atkinson, A.B. (1970): On the Measurment of Inequality, in: Journal of Economic Theory, Vol. 2(3), pp. 244-263.
ineq , atkinson , atkinson.den
theta<-c(2,1,5,2) x<- c(rgamma(50000,2,1),rgamma(50000,5,2)) para<-1 data(dist.para.t) atkinson.md(10000,para,"gamma","gamma",theta,0,0.5,0.5,dist.para.t,zero.approx=1)$AIM atkinson(x,1)theta<-c(2,1,5,2) x<- c(rgamma(50000,2,1),rgamma(50000,5,2)) para<-1 data(dist.para.t) atkinson.md(10000,para,"gamma","gamma",theta,0,0.5,0.5,dist.para.t,zero.approx=1)$AIM atkinson(x,1)
This function yields the cdf of a mixture distribution consisting of a point mass (at the lower end), a uniform distribution (above the point mass and below the Dagum distribution) and a Dagum distribution.
cdf.mix.dag(q, pi0, thres0 = 0, pi1, thres1, mu, sigma, nu, tau)cdf.mix.dag(q, pi0, thres0 = 0, pi1, thres1, mu, sigma, nu, tau)
q |
a vector of quantiles. |
pi0 |
the probability mass at thres0. |
thres0 |
the location of the probability mass at the lower end of the distribution. |
pi1 |
the probability mass of the uniform distribution. |
thres1 |
the upper bound of the uniform distribution. |
mu |
the parameter mu of the Dagum distribution as defined by the function GB2. |
sigma |
the parameter sigma of the Dagum distribution as defined by the function GB2. |
nu |
the parameter nu of the Dagum distribution as defined by the function GB2. |
tau |
the parameter tau of the Dagum distribution as defined by the function GB2. |
returns the cumulative density for the given quantiles.
Alexander Sohn
Sohn, A., Klein, N. and Kneib. T. (2014): A New Semiparametric Approach to Analysing Conditional Income Distributions, in: SOEPpapers, No. 676.
pi0.s<-0.2 pi1.s<-0.1 thres0.s<-0 thres1.s<-25000 mu.s<-20000 sigma.s<-5 nu.s<-0.5 tau.s<-1 cdf.mix.dag(50000,pi0.s,thres0.s,pi1.s,thres1.s,mu.s,sigma.s,nu.s,tau.s)pi0.s<-0.2 pi1.s<-0.1 thres0.s<-0 thres1.s<-25000 mu.s<-20000 sigma.s<-5 nu.s<-0.5 tau.s<-1 cdf.mix.dag(50000,pi0.s,thres0.s,pi1.s,thres1.s,mu.s,sigma.s,nu.s,tau.s)
This function yields the cdf of a mixture distribution consisting of a point mass (at the lower end), a uniform distribution (above the point mass and below the log-normal distribution) and a log-normal distribution.
cdf.mix.LN(q, pi0, thres0 = 0, pi1, thres1, mu, sigma)cdf.mix.LN(q, pi0, thres0 = 0, pi1, thres1, mu, sigma)
q |
a vector of quantiles. |
pi0 |
the probability mass at thres0. |
thres0 |
the location of the probability mass at the lower end of the distribution. |
pi1 |
the probability mass of the uniform distribution. |
thres1 |
the upper bound of the uniform distribution. |
mu |
the parameter mu of the Dagum distribution as defined by the function GB2. |
sigma |
the parameter sigma of the Dagum distribution as defined by the function GB2. |
returns the cumulative density for the given quantiles.
Alexander Sohn
Sohn, A., Klein, N., Kneib. T. (2014): A New Semiparametric Approach to Analysing Conditional Income Distributions, in: SOEPpapers, No. 676.
pi0.s<-0.2 pi1.s<-0.1 thres0.s<-0 thres1.s<-25000 mu.s<-10 sigma.s<-2 cdf.mix.LN(50000,pi0.s,thres0.s,pi1.s,thres1.s,mu.s,sigma.s)pi0.s<-0.2 pi1.s<-0.1 thres0.s<-0 thres1.s<-25000 mu.s<-10 sigma.s<-2 cdf.mix.LN(50000,pi0.s,thres0.s,pi1.s,thres1.s,mu.s,sigma.s)
This function computes the Coefficient of Variation for a vector of observations.
coeffvar(x)coeffvar(x)
x |
a vector of observations. |
cv |
returns the coefficient of variation without bias correction. |
bccv |
returns the coefficient of variation with bias correction. |
Weighting is not properly accounted for in the sample adjustment of bccv!
Alexander Sohn
Atkinson, A.B. and Bourguignon, F. (2000): Income Distribution and Economics, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 1-86, Elsevier, Amsterdam.
# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) coeffvar(x)# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) coeffvar(x)
This function computes simultaneous confidence bands for samples of the presumed distribution of the parameter estimator.
confband.kneib(samples, level = 0.95)confband.kneib(samples, level = 0.95)
samples |
matrix containing samples of the presumed distribution of the parameter estimator. |
level |
the desired confidence level. |
lower |
a vector containing the lower bound of the confidence band. |
upper |
a vector containing the lower bound of the confidence band. |
This function is taken from the work of T. Krivobokova, T. Kneib and G. Claeskens.
Alexander Sohn
T. Krivobokova, T. Kneib, G. Claeskens (2010): Simultaneous Confidence Bands for Penalized Spline Estimators, in: Journal of the American Statistical Association, Vol. 105(490), pp.852-863.
mu<-1:20 n<-1000 mcmc<-matrix(NA,n,20) for(i in 1:20){ mcmc[,i]<- rnorm(n,mu[i],sqrt(i)) } plot(mu,type="l",ylim=c(-10,30),lwd=3) lines(confband.pw(mcmc)$lower,lty=2) lines(confband.pw(mcmc)$upper,lty=2) lines(confband.kneib(mcmc)$lower,lty=3) lines(confband.kneib(mcmc)$upper,lty=3)mu<-1:20 n<-1000 mcmc<-matrix(NA,n,20) for(i in 1:20){ mcmc[,i]<- rnorm(n,mu[i],sqrt(i)) } plot(mu,type="l",ylim=c(-10,30),lwd=3) lines(confband.pw(mcmc)$lower,lty=2) lines(confband.pw(mcmc)$upper,lty=2) lines(confband.kneib(mcmc)$lower,lty=3) lines(confband.kneib(mcmc)$upper,lty=3)
This function computes pointwise confidence bands for samples of the presumed distribution of the parameter estimator.
confband.pw(samples, level = 0.95)confband.pw(samples, level = 0.95)
samples |
matrix containing samples of the presumed distribution of the parameter estimator. |
level |
the desired confidence level. |
lower |
a vector containing the lower bound of the confidence band. |
upper |
a vector containing the lower bound of the confidence band. |
This function is mainly derived from the work of T. Krivobokova, T. Kneib and G. Claeskens.
Alexander Sohn
T. Krivobokova, T. Kneib, G. Claeskens (2010): Simultaneous Confidence Bands for Penalized Spline Estimators, in: Journal of the American Statistical Association, Vol. 105(490), pp.852-863.
mu<-1:20 n<-1000 mcmc<-matrix(NA,n,20) for(i in 1:20){ mcmc[,i]<- rnorm(n,mu[i],sqrt(i)) } plot(mu,type="l",ylim=c(-10,30),lwd=3) lines(confband.pw(mcmc)$lower,lty=2) lines(confband.pw(mcmc)$upper,lty=2) lines(confband.kneib(mcmc)$lower,lty=3) lines(confband.kneib(mcmc)$upper,lty=3)mu<-1:20 n<-1000 mcmc<-matrix(NA,n,20) for(i in 1:20){ mcmc[,i]<- rnorm(n,mu[i],sqrt(i)) } plot(mu,type="l",ylim=c(-10,30),lwd=3) lines(confband.pw(mcmc)$lower,lty=2) lines(confband.pw(mcmc)$upper,lty=2) lines(confband.kneib(mcmc)$lower,lty=3) lines(confband.kneib(mcmc)$upper,lty=3)
This is some simulated income data from a mixture model as used in Sohn et al (2014).
data(dat)data(dat)
The format is: List of 4 $ dag.para:'data.frame': 8 obs. of 1 variable: ..$ parameters: num [1:8] 0.2 0.1 0 25000 20000 5 0.5 1 $ dag.s :'data.frame': 100 obs. of 3 variables: ..$ cat: int [1:100] 3 1 3 1 2 3 3 1 3 3 ... ..$ y : num [1:100] 36410 0 58165 0 15034 ... ..$ w : int [1:100] 1 1 1 2 1 3 2 1 1 1 ... $ LN.para :'data.frame': 6 obs. of 1 variable: ..$ parameters: num [1:6] 0.2 0.1 0 25000 10 2 $ LN.s :'data.frame': 100 obs. of 3 variables: ..$ cat: int [1:100] 3 3 1 3 3 3 3 3 3 3 ... ..$ y : num [1:100] 29614 29549 0 33068 463941 ... ..$ w : int [1:100] 1 2 1 1 1 1 1 2 1 1 ...
The data contains information on whether the person is unemployed (cat=1), precariously employed (cat=2) or in standard employment(cat=3), the corresponding parameters used to generate the truncated distribution - both for Log-normal and Dagum.
Sohn, A., Klein, N., Kneib. T. (2014): A New Semiparametric Approach to Analysing Conditional Income Distributions, in: SOEPpapers, No. 676.
data(dat) str(dat)data(dat) str(dat)
This function computes the p-value for a mixture of two continuous income distributions and a point mass for zero-incomes.
den.md(y, dist1, dist2, theta, p0, p1, p2, dist.para.table)den.md(y, dist1, dist2, theta, p0, p1, p2, dist.para.table)
y |
a vector with incomes. If a zero income is included, it must be the first element. |
dist1 |
character string with the name of the first continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
dist2 |
character string with the name of the second continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
theta |
vector with the parameters of dist1 and dist2. Order must be the same as in the functions for the distributions. |
p0 |
scalar with probability mass for the point mass. |
p1 |
scalar with probability mass for dist1. |
p2 |
scalar with probability mass for dist2. |
dist.para.table |
a table of the same form as |
returns the density for given values of y.
Alexander Sohn
data(dist.para.t) ygrid<-seq(0,20,by=0.1)#c(seq(0,1e5,by=100),seq(1.1e5,1e6,by=100000)) theta<-c(5,1,10,1.5) p0<-0.2 p1<-0.3 p2<-0.5 n <-100000 y.sim <- ysample.md(n, "norm", "norm", theta, p0, p1, p2, dist.para.t) den<-den.md(ygrid,"norm", "norm", theta, p0, p1, p2, dist.para.table=dist.para.t) hist(y.sim,freq=FALSE) #hist(y.sim,breaks=c(seq(0,1e5,by=100),seq(1.1e5,1e6,by=100000)),xlim=c(0,2e4),ylim=c(0,0.001)) lines(ygrid,den,col=2)data(dist.para.t) ygrid<-seq(0,20,by=0.1)#c(seq(0,1e5,by=100),seq(1.1e5,1e6,by=100000)) theta<-c(5,1,10,1.5) p0<-0.2 p1<-0.3 p2<-0.5 n <-100000 y.sim <- ysample.md(n, "norm", "norm", theta, p0, p1, p2, dist.para.t) den<-den.md(ygrid,"norm", "norm", theta, p0, p1, p2, dist.para.table=dist.para.t) hist(y.sim,freq=FALSE) #hist(y.sim,breaks=c(seq(0,1e5,by=100),seq(1.1e5,1e6,by=100000)),xlim=c(0,2e4),ylim=c(0,0.001)) lines(ygrid,den,col=2)
A data frame providing information on the number of parameters of distributions used for analysing conditional income distributions.
data(dist.para.t)data(dist.para.t)
A data frame with the following 3 variables.
Distributionname of the distribution.
distfunction of the distribution.
Parametersthe number of parameters for the distribution.
data(dist.para.t) dist.para.tdata(dist.para.t) dist.para.t
This function computes the Measures of the Generalised Entropy Family for a vector of observations.
entropy(x, alpha = 1)entropy(x, alpha = 1)
x |
a vector of observations. |
alpha |
the parameter for the generalised entropy family of measures, denoted by alpha by Cowell (2000). Note that this parameter notation differs from the notation used in the ineq package. |
returns the entropy measure.
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 1-86, Elsevier, Amsterdam.
# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) entropy(x)# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) entropy(x)
This function computes four standard entropy measures from the generalised entropy class of inequality indices (I(alpha)) for Generalised Beta Distribution of Second Kind, namely the mean logarithmic deviation (I(0)), the Theil index (I(1)) as well as a bottom-sensitive index (I(-1)) and a top-sensitive index (I(2)). For other values of alpha, the function provides a numerical approximation.
entropy.GB2(b, a, p, q, alpha = NULL, ylim = c(0, 1e+06), zeroapprox = 0.01)entropy.GB2(b, a, p, q, alpha = NULL, ylim = c(0, 1e+06), zeroapprox = 0.01)
b |
the parameter b of the Dagum distribution as defined by Kleiber and Kotz (2003). |
a |
the parameter a of the Dagum distribution as defined by Kleiber and Kotz (2003). |
p |
the parameter p of the Dagum distribution as defined by Kleiber and Kotz (2003). |
q |
the parameter q of the Dagum distribution as defined by Kleiber and Kotz (2003). |
alpha |
measure for the entropy measure as denoted by Cowell (2000). The default is alpha=1, i.e. the Theil Index. |
ylim |
limits of the interval of y considered needed for the approximation of the entropy measure. The default is [0,1e+06]. |
zeroapprox |
an approximation for zero needed for the approximation of the entropy measure. The default is 0.01. |
returns the selected entropy measure.
Alexander Sohn
Kleiber, C. and Kotz, S. (2003): Statistical Size Distributions in Economics and Actuarial Sciences, Wiley, Hoboken.
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 87-166, Elsevier, Amsterdam.
Jenkins, S.P. (2009): Distributionally-Sensitive Inequality Indices and the GB2 Income Distribution, in: Review of Income and Wealth, Vol. 55(2), pp.392-398.
a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(1000,b.test,a.test,p.test,q.test) entropy.GB2(b.test,a.test,p.test,q.test,alpha=alpha.test,ylim=c(0,1e+07)) entropy(GB2sample, alpha.test)a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(1000,b.test,a.test,p.test,q.test) entropy.GB2(b.test,a.test,p.test,q.test,alpha=alpha.test,ylim=c(0,1e+07)) entropy(GB2sample, alpha.test)
This function uses Monte Carlo methods to estimate an entropy measure for a mixture of two continuous income distributions and a point mass for zero-incomes.
entropy.md(n, alpha = 1, dist1, dist2, theta, p0, p1, p2, dist.para.table, zero.approx)entropy.md(n, alpha = 1, dist1, dist2, theta, p0, p1, p2, dist.para.table, zero.approx)
n |
sample size used to estimate the entropy measure. |
alpha |
the parameter for the generalised entropy family of measures, denoted by alpha by Cowell (2000). Note that this parameter notation differs from the notation used in the ineq package. |
dist1 |
character string with the name of the first continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
dist2 |
character string with the name of the second continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
theta |
vector with the parameters of dist1 and dist2. Order must be the same as in the functions for the distributions. |
p0 |
scalar with probability mass for the point mass. |
p1 |
scalar with probability mass for dist1. |
p2 |
scalar with probability mass for dist2. |
dist.para.table |
a table of the same form as |
zero.approx |
a scalar which replaces zero-incomes (and negative incomes), such that entropy measures involving a logarithm return finite values. |
entropy |
the estimated entropy measure. |
alpha |
the entropy parameter used. |
y |
a vector with the simulated incomes to estimate the entropy measure. |
y2 |
a vector with the zero-replaced simulated incomes to estimate the entropy measure. |
zero.replace |
a logical vector indicating whether a zero has been replaced. |
stat |
a vector with the simulated group the observation was chosen from. 0 is the point mass, 1 dist1 and 2 dist2. |
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 87-166, Elsevier, Amsterdam.
theta<-c(2,1,5,2) x<- c(rgamma(500,2,1),rgamma(500,5,2)) para<-1 entropy(x,para) data(dist.para.t) entropy.md(100,para,"gamma","gamma",theta,0,0.5,0.5,dist.para.t,zero.approx=1)$entropytheta<-c(2,1,5,2) x<- c(rgamma(500,2,1),rgamma(500,5,2)) para<-1 entropy(x,para) data(dist.para.t) entropy.md(100,para,"gamma","gamma",theta,0,0.5,0.5,dist.para.t,zero.approx=1)$entropy
This function computes fractional ranks which are required for the S-Gini coefficient.
frac.ranks(x, w = NULL)frac.ranks(x, w = NULL)
x |
a vector with sorted income values. |
w |
a vector of weights. |
returns the fractional ranks.
Alexander Sohn
van Kerm, P. (2009): sgini - Generalized Gini and Concentration coefficients (with factor decomposition) in Stata', CEPS/INSTEAD, Differdange, Luxembourg.
This function computes the Gini coefficient for a vector of observations.
gini(x)gini(x)
x |
a vector of observations. |
Gini |
the Gini coefficient for the sample. |
bcGini |
the bias-corrected Gini coefficient for the sample. |
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 87-166, Elsevier, Amsterdam.
# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) gini(x)# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) gini(x)
This function computes the Gini coefficient for the Dagum Distribution.
gini.Dag(a, p)gini.Dag(a, p)
a |
the parameter a of the Dagum distribution as defined by Kleiber and Kotz (2003). |
p |
the parameter p of the Dagum distribution as defined by Kleiber and Kotz (2003). |
returns the Gini coefficient.
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 1-86, Elsevier, Amsterdam.
a.test<- 4 b.test<- 20000 p.test<- 0.7 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,1) gini.Dag(a.test,p.test) gini(GB2sample)a.test<- 4 b.test<- 20000 p.test<- 0.7 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,1) gini.Dag(a.test,p.test) gini(GB2sample)
This function approximates the Gini coefficient for a distribution specified by a vector of densities and a corresponding income vector. A point mass at zero is allowed.
gini.den(incs, dens, pm0 = NA, lower = NULL, upper = NULL)gini.den(incs, dens, pm0 = NA, lower = NULL, upper = NULL)
incs |
a vector with sorted income values. |
dens |
a vector with the corresponding densities. |
pm0 |
the point mass for zero incomes. If not specified no point mass is assumed. |
lower |
the lower bound of the income range considered. |
upper |
the upper bound of the income range considered. |
Gini |
the approximation of the Gini coefficient. |
pm0 |
the point mass for zero incomes used. |
lower |
the lower bound of the income range considered used. |
upper |
the upper bound of the income range considered used. |
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 1-86, Elsevier, Amsterdam.
mu<-2 sigma<-1 incs<-c(seq(0,500,by=0.01),seq(501,50000,by=1)) dens<-dLOGNO(incs,mu,sigma) plot(incs,dens,type="l",xlim=c(0,100)) gini.den(incs=incs,dens=dens)$Gini gini(rLOGNO(5000000,mu,sigma))$Gini 2*pnorm(sigma/sqrt(2))-1 #theoretical Ginimu<-2 sigma<-1 incs<-c(seq(0,500,by=0.01),seq(501,50000,by=1)) dens<-dLOGNO(incs,mu,sigma) plot(incs,dens,type="l",xlim=c(0,100)) gini.den(incs=incs,dens=dens)$Gini gini(rLOGNO(5000000,mu,sigma))$Gini 2*pnorm(sigma/sqrt(2))-1 #theoretical Gini
This function computes the Gini coefficient for the gamma distribution.
gini.gamma(p)gini.gamma(p)
p |
the shape parameter p of the gamma distribution as defined by Kleiber and Kotz (2003). |
returns the Gini coefficient.
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 1-86, Elsevier, Amsterdam.
Kleiber, C. and Kotz, S. (2003): Statistical Size Distributions in Economics and Actuarial Sciences, Wiley, Hoboken.
shape.test <- 5 scale.test <- 50000 y <- rgamma(10000,shape=shape.test,scale=scale.test) gini(y) gini.gamma(shape.test)shape.test <- 5 scale.test <- 50000 y <- rgamma(10000,shape=shape.test,scale=scale.test) gini(y) gini.gamma(shape.test)
This function uses Monte Carlo methods to estimate the Gini coefficient for a mixture of two continuous income distributions and a point mass for zero-incomes.
gini.md(n, dist1, dist2, theta, p0, p1, p2, dist.para.table)gini.md(n, dist1, dist2, theta, p0, p1, p2, dist.para.table)
n |
sample size used to estimate the gini coefficient. |
dist1 |
character string with the name of the first continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
dist2 |
character string with the name of the second continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
theta |
vector with the parameters of dist1 and dist2. Order must be the same as in the functions for the distributions. |
p0 |
scalar with probability mass for the point mass. |
p1 |
scalar with probability mass for dist1. |
p2 |
scalar with probability mass for dist2. |
dist.para.table |
a table of the same form as |
gini |
the estimated Gini coefficient. |
y |
a vector with the simulated incomes to estimate the Gini coefficient. |
stat |
a vector with the simulated group the observation was chosen from. 0 is the point mass, 1 dist1 and 2 dist2. |
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 87-166, Elsevier, Amsterdam.
theta<-c(2,1,5,2) x<- c(rnorm(500,2,1),rnorm(500,5,2)) gini(x)$Gini data(dist.para.t) gini.md(1000,"norm","norm",theta,0,0.5,0.5,dist.para.t)$ginitheta<-c(2,1,5,2) x<- c(rnorm(500,2,1),rnorm(500,5,2)) gini(x)$Gini data(dist.para.t) gini.md(1000,"norm","norm",theta,0,0.5,0.5,dist.para.t)$gini
This function uses Monte Carlo methods to estimate an the mean logarithmic deviation, the Theil Index and the Gini Coefficient for a mixture of two continuous income distributions and a point mass for zero-incomes.
ineq.md(n, dist1, dist2, theta, p0, p1, p2, dist.para.table, zero.approx)ineq.md(n, dist1, dist2, theta, p0, p1, p2, dist.para.table, zero.approx)
n |
sample size used to estimate the gini coefficient. |
dist1 |
character string with the name of the first continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
dist2 |
character string with the name of the second continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
theta |
vector with the parameters of dist1 and dist2. Order must be the same as in the functions for the distributions. |
p0 |
scalar with probability mass for the point mass. |
p1 |
scalar with probability mass for dist1. |
p2 |
scalar with probability mass for dist2. |
dist.para.table |
a table of the same form as |
zero.approx |
a scalar which replaces zero-incomes (and negative incomes), such that entropy measures involving a logarithm return finite values. |
MLD |
the estimated mean logarithmic deviation. |
Theil |
the estimated Theil index. |
Gini |
the estimated Gini coefficient. |
y |
a vector with the simulated incomes to estimate the entropy measure. |
y2 |
a vector with the zero-replaced simulated incomes to estimate the entropy measure. |
zero.replace |
a logical vector indicating whether a zero has been replaced. |
stat |
a vector with the simulated group the observation was chosen from. 0 is the point mass, 1 dist1 and 2 dist2. |
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 87-166, Elsevier, Amsterdam.
theta<-c(0,1,5,2) x<- c(rgamma(500,2,1),rgamma(500,5,2)) entropy(x,0) entropy(x,1) gini(x)$Gini data(dist.para.t) im<-ineq.md(100,"gamma","gamma",theta,0,0.5,0.5,dist.para.t,zero.approx=1) im$MLD im$Theil im$Ginitheta<-c(0,1,5,2) x<- c(rgamma(500,2,1),rgamma(500,5,2)) entropy(x,0) entropy(x,1) gini(x)$Gini data(dist.para.t) im<-ineq.md(100,"gamma","gamma",theta,0,0.5,0.5,dist.para.t,zero.approx=1) im$MLD im$Theil im$Gini
Calculates the k-th moment of the Generalised Beta Distribution of Second Kind.
km.GB2(b, a, p, q, k)km.GB2(b, a, p, q, k)
b |
the parameter b of the Dagum distribution as defined by Kleiber and Kotz (2003). |
a |
the parameter a of the Dagum distribution as defined by Kleiber and Kotz (2003). |
p |
the parameter p of the Dagum distribution as defined by Kleiber and Kotz (2003). |
q |
the parameter q of the Dagum distribution as defined by Kleiber and Kotz (2003). |
k |
order of the moment desired. |
returns the k-th moment.
Alexander Sohn
Kleiber, C. and Kotz, S. (2003): Statistical Size Distributions in Economics and Actuarial Sciences, Wiley, Hoboken.
a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,q.test) km.GB2(b.test,a.test,p.test,q.test,k=1) mean(GB2sample)a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,q.test) km.GB2(b.test,a.test,p.test,q.test,k=1) mean(GB2sample)
This function plots a graph entailing the empirical cdf and the parametrically specified cdf composed of a mixture distribution either by cdf.mix.dag or cdf.mix.LN.
midks.plot(x.seq, y, dist, w.emp = NULL, ...)midks.plot(x.seq, y, dist, w.emp = NULL, ...)
x.seq |
the sequence on the x-axis for which the parametric distribution is plotted. |
y |
a vector of observed incomes. |
dist |
a function specifying the parametric cdf. |
w.emp |
the weights of the observations contained in y. |
... |
arguments to be passed to dist. |
Alexander sohn
midks.test,cdf.mix.dag ,cdf.mix.LN
# parameter values pi0.s<-0.2 pi1.s<-0.1 thres0.s<-0 thres1.s<-25000 mu.s<-20000 sigma.s<-5 nu.s<-0.5 tau.s<-1 x.seq<-seq(0,200000,by=1000) # generate sample n<-100 s<-as.data.frame(matrix(NA,n,3)) names(s)<-c("cat","y","w") s[,1]<-sample(1:3,n,replace=TRUE,prob=c(pi0.s,pi1.s,1-pi0.s-pi1.s)) s[,3]<-rep(1,n) for(i in 1:n){ if(s$cat[i]==1){s$y[i]<-0 }else if(s$cat[i]==2){s$y[i]<-runif(1,thres0.s,thres1.s) }else s$y[i]<-rGB2(1,mu=mu.s,sigma=sigma.s,nu=nu.s,tau=tau.s)+thres1.s } # display midks.plot(x.seq,s$y,dist=cdf.mix.dag,pi0=pi0.s,thres0=thres0.s,pi1=pi1.s, thres1=thres1.s,mu=mu.s,sigma=sigma.s,nu=nu.s,tau=tau.s)# parameter values pi0.s<-0.2 pi1.s<-0.1 thres0.s<-0 thres1.s<-25000 mu.s<-20000 sigma.s<-5 nu.s<-0.5 tau.s<-1 x.seq<-seq(0,200000,by=1000) # generate sample n<-100 s<-as.data.frame(matrix(NA,n,3)) names(s)<-c("cat","y","w") s[,1]<-sample(1:3,n,replace=TRUE,prob=c(pi0.s,pi1.s,1-pi0.s-pi1.s)) s[,3]<-rep(1,n) for(i in 1:n){ if(s$cat[i]==1){s$y[i]<-0 }else if(s$cat[i]==2){s$y[i]<-runif(1,thres0.s,thres1.s) }else s$y[i]<-rGB2(1,mu=mu.s,sigma=sigma.s,nu=nu.s,tau=tau.s)+thres1.s } # display midks.plot(x.seq,s$y,dist=cdf.mix.dag,pi0=pi0.s,thres0=thres0.s,pi1=pi1.s, thres1=thres1.s,mu=mu.s,sigma=sigma.s,nu=nu.s,tau=tau.s)
This function performs a Kolmogorov-Smirnov test for a parametrically specified cdf composed of a mixture distribution either by cdf.mix.dag or cdf.mix.LN.
midks.test(x, y, ..., w = NULL, pmt = NULL)midks.test(x, y, ..., w = NULL, pmt = NULL)
x |
a vector of observed incomes. |
y |
a function specifying the parametric cdf. |
... |
arguments to be passed to y. |
w |
the weights of the observations contained in y. |
pmt |
point mass threshold equivalent to thres0 in y. |
statistic |
returns the test statistic. |
method |
returns the methodology - currently always One-sample KS-test. |
diffpm |
the difference of the probability for the point mass. |
diff1 |
the upper difference between for the continuous part of the cdfs. |
diff2 |
the lower difference between for the continuous part of the cdfs. |
Alexander Sohn
Sohn, A., Klein, N. and Kneib. T. (2014): A New Semiparametric Approach to Analysing Conditional Income Distributions, in: SOEPpapers, No. 676.
# parameter values pi0.s<-0.2 pi1.s<-0.1 thres0.s<-0 thres1.s<-25000 mu.s<-20000 sigma.s<-5 nu.s<-0.5 tau.s<-1 # generate sample n<-100 s<-as.data.frame(matrix(NA,n,3)) names(s)<-c("cat","y","w") s[,1]<-sample(1:3,n,replace=TRUE,prob=c(pi0.s,pi1.s,1-pi0.s-pi1.s)) s[,3]<-rep(1,n) for(i in 1:n){ if(s$cat[i]==1){s$y[i]<-0 }else if(s$cat[i]==2){s$y[i]<-runif(1,thres0.s,thres1.s) }else s$y[i]<-rGB2(1,mu=mu.s,sigma=sigma.s,nu=nu.s,tau=tau.s)+thres1.s } # midks.test midks.test(s$y,cdf.mix.dag,pi0=pi0.s,thres0=thres0.s,pi1=pi1.s,thres1=thres1.s,mu=mu.s, sigma=sigma.s,nu=nu.s,tau=tau.s,w=s$w,pmt=thres0.s)$statistic# parameter values pi0.s<-0.2 pi1.s<-0.1 thres0.s<-0 thres1.s<-25000 mu.s<-20000 sigma.s<-5 nu.s<-0.5 tau.s<-1 # generate sample n<-100 s<-as.data.frame(matrix(NA,n,3)) names(s)<-c("cat","y","w") s[,1]<-sample(1:3,n,replace=TRUE,prob=c(pi0.s,pi1.s,1-pi0.s-pi1.s)) s[,3]<-rep(1,n) for(i in 1:n){ if(s$cat[i]==1){s$y[i]<-0 }else if(s$cat[i]==2){s$y[i]<-runif(1,thres0.s,thres1.s) }else s$y[i]<-rGB2(1,mu=mu.s,sigma=sigma.s,nu=nu.s,tau=tau.s)+thres1.s } # midks.test midks.test(s$y,cdf.mix.dag,pi0=pi0.s,thres0=thres0.s,pi1=pi1.s,thres1=thres1.s,mu=mu.s, sigma=sigma.s,nu=nu.s,tau=tau.s,w=s$w,pmt=thres0.s)$statistic
A list containing parameter estimates as obtained from Structured Additive Distributional Regression
data(params)data(params)
The format is: List of 16 $ mcmcsize : num 1000 $ ages : int [1:40] 21 22 23 24 25 26 27 28 29 30 ... $ unems : num [1:23] 0 1 2 3 4 5 6 7 8 9 ... $ educlvls : num [1:2] -1 1 $ bulas : chr [1:16] "SH" "HH" "NDS" "Bremen" ... $ aft.v : num [1:3447] 4.85 6.5 5.92 5.76 6.05 ... $ bft.v : num [1:3447] 78169 65520 47184 58763 46188 ... $ cft.v : num [1:3447] 1.177 0.299 0.818 0.522 0.836 ... $ mupt.v : num [1:3447] 10.21 9.46 9.66 9.77 9.68 ... $ sigmapt.v: num [1:3447] 1.07 1.25 1.85 1.21 1.74 ... $ muemp.v : num [1:3447] 3.25 2.68 2.08 3.53 2.43 ... $ muunemp.v: num [1:3447] -2.691 -0.813 -1.919 -1.542 -1.765 ... $ punemp.v : num [1:3447] 0.0658 0.3104 0.1314 0.18 0.1496 ... $ pemp.v : num [1:3447] 0.934 0.69 0.869 0.82 0.85 ... $ pft.v : num [1:3447] 0.898 0.644 0.77 0.796 0.78 ... $ ppt.v : num [1:3447] 0.0359 0.0452 0.0987 0.024 0.0706 ...
data(params) str(params) ## maybe str(params) ; plot(params) ...data(params) str(params) ## maybe str(params) ; plot(params) ...
This function plots Pen's parade.
pens.parade(x, bodies = TRUE, feet = 0, ...)pens.parade(x, bodies = TRUE, feet = 0, ...)
x |
a vector of observed incomes. |
bodies |
a logical value indicating whether lines, i.e. the bodies, should be drawn. |
feet |
a numeric value indicating where the lines originate. |
... |
additional arguments passed to the plot function. |
Alexander Sohn
Atkinson, A.B. (1975): The Economics of Inequality, Cleardon Press, Oxford.
a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(100,b.test,a.test,p.test,q.test) pens.parade( GB2sample)a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(100,b.test,a.test,p.test,q.test) pens.parade( GB2sample)
This function computes the polarisation measure proposed in Esteban, Gradin and Ray (2007) which accounts for deviations from an n-spike representation of strata in society.
polarisation.EGR(alpha, beta, rho, y, f = NULL, dist = NULL, weights = NULL, pm0 = NA, lower = NULL, upper = NULL, ...)polarisation.EGR(alpha, beta, rho, y, f = NULL, dist = NULL, weights = NULL, pm0 = NA, lower = NULL, upper = NULL, ...)
alpha |
a scalar containing the alpha parameter from Esteban and Ray (1994) on the sensitivity to polarisation. |
beta |
a scalar containing the beta parameter from Esteban, Gradin and Ray (2007) on the weight assigned to the error in the n-spike representation. |
rho |
a dataframe with the group means in the first column and their respective population shares in the second. The groups need to be exogenously defined. Note: the two columns should be named |
y |
a vector of incomes. If f is NULL and dist is NULL, this includes all incomes of all observations in the sample, i.e. all observations comprising the aggregate distribution. If either f or dist is not NULL, then this gives the incomes where the density is evaluated. |
f |
a vector of user-defined densities of the aggregate distribution for the given incomes in y. |
dist |
character string with the name of the distribution used. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
weights |
an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector. If non-NULL, observations in y are weighted accordingly. |
pm0 |
the point mass for zero incomes used in the gini.den function. If not specified no point mass is assumed. |
lower |
the lower bound of the income range considered used in the gini.den function. |
upper |
the upper bound of the income range considered used in the gini.den function. |
... |
arguments to be passed to the distribution function used, e.g. mean and sd for the normal distribution. |
P |
the polarisation measure proposed by Esteban, Gradin and Ray (2007). |
PG |
the adjusted polarisation measure proposed by Gradin (2000). |
alpha |
the alpha parameter used. |
beta |
the beta parameter used. |
beta |
the distribution option used, i.e. whether only y, f or dist was used. |
Alexander Sohn
Esteban, J. and Ray, D. (1994): On the Measurment of Polarization, in: Econometrica, Vol. 62(4), pp. 819-851.
Esteban, J., Gradin, C. and Ray, D. (2007): Extensions of a Measure of Polarization, with an Application to the Income Distribution of five OECD Countries.
Gradin, C. (2000): Polarization by Sub-populations in Spain, 1973-91, in Review of Income and Wealth, Vol. 46(4), pp.457-474.
## example 1 y<-rnorm(1000,5,0.5) y<-sort(y) m.y<-mean(y) sd.y<-sd(y) y1<-y[1:(length(y)/4)] m.y1<-mean(y1) sd.y1<-sd(y1) y2<-y[(length(y)/4+1):length(y)] m.y2<-mean(y2) sd.y2<-sd(y2) means<-c(m.y1,m.y2) share1<- length(y1)/length(y) share2<- length(y2)/length(y) shares<- c(share1,share2) rho<-data.frame(means=means,shares=shares) alpha<-1 beta<-1 den<-density(y) polarisation.ER(alpha,rho,comp=FALSE) polarisation.EGR(alpha,beta,rho,y)$P polarisation.EGR(alpha,beta,rho,y=den$x,f=den$y)$P polarisation.EGR(alpha,beta,rho,y=seq(0,10,by=0.1),dist="norm", mean=m.y,sd=sd.y)$P polarisation.EGR(alpha,beta,rho,y=seq(0,10,by=0.1),dist="norm", mean=m.y,sd=sd.y)$PG ## example 2 y1<-rnorm(100,5,1) y2<-rnorm(100,1,0.1) y <- c(y1,y2) m.y1<-mean(y1) sd.y1<-sd(y1) m.y2<-mean(y2) sd.y2<-sd(y2) means<-c(m.y1,m.y2) share1<- length(y1)/length(y) share2<- length(y2)/length(y) shares<- c(share1,share2) rho<-data.frame(means=means,shares=shares) alpha<-1 beta<-1 polarisation.EGR(alpha,beta,rho,y=seq(0,10,by=0.1),dist="norm", mean=c(m.y1,m.y2),sd=c(sd.y1,sd.y2))$P## example 1 y<-rnorm(1000,5,0.5) y<-sort(y) m.y<-mean(y) sd.y<-sd(y) y1<-y[1:(length(y)/4)] m.y1<-mean(y1) sd.y1<-sd(y1) y2<-y[(length(y)/4+1):length(y)] m.y2<-mean(y2) sd.y2<-sd(y2) means<-c(m.y1,m.y2) share1<- length(y1)/length(y) share2<- length(y2)/length(y) shares<- c(share1,share2) rho<-data.frame(means=means,shares=shares) alpha<-1 beta<-1 den<-density(y) polarisation.ER(alpha,rho,comp=FALSE) polarisation.EGR(alpha,beta,rho,y)$P polarisation.EGR(alpha,beta,rho,y=den$x,f=den$y)$P polarisation.EGR(alpha,beta,rho,y=seq(0,10,by=0.1),dist="norm", mean=m.y,sd=sd.y)$P polarisation.EGR(alpha,beta,rho,y=seq(0,10,by=0.1),dist="norm", mean=m.y,sd=sd.y)$PG ## example 2 y1<-rnorm(100,5,1) y2<-rnorm(100,1,0.1) y <- c(y1,y2) m.y1<-mean(y1) sd.y1<-sd(y1) m.y2<-mean(y2) sd.y2<-sd(y2) means<-c(m.y1,m.y2) share1<- length(y1)/length(y) share2<- length(y2)/length(y) shares<- c(share1,share2) rho<-data.frame(means=means,shares=shares) alpha<-1 beta<-1 polarisation.EGR(alpha,beta,rho,y=seq(0,10,by=0.1),dist="norm", mean=c(m.y1,m.y2),sd=c(sd.y1,sd.y2))$P
This function computes the polarisation measure proposed in Esteban and and Ray (1994).
polarisation.ER(alpha, rho, comp = FALSE)polarisation.ER(alpha, rho, comp = FALSE)
alpha |
a scalar containing the alpha parameter from Esteban and Ray (1994) on the sensitivity to polarisation. |
rho |
a dataframe with the group means in the first column and their respective population shares in the second. The groups need to be exogenously defined. Note: the two columns should be named |
comp |
logical; if TRUE, all components pf p_i^(a+alpha)*p_j*abs(y_i-y_j) |
P |
the polarisation measure proposed by Esteban and Ray (1994). |
means |
the means stored in rho. |
shares |
the shares stored in rho.. |
ERcomp |
if comp is TRUE, the components aggregated in P. |
Alexander Sohn
Esteban, J. and Ray, D. (1994): On the Measurment of Polarization, in: Econometrica, Vol. 62(4), pp. 819-851.
means<-rnorm(10)+5 shares<- rep(1,length(means)) shares<-shares/sum(shares) rho<-data.frame(means=means,shares=shares) alpha<-1 polarisation.ER(alpha,rho,comp=FALSE)means<-rnorm(10)+5 shares<- rep(1,length(means)) shares<-shares/sum(shares) rho<-data.frame(means=means,shares=shares) alpha<-1 polarisation.ER(alpha,rho,comp=FALSE)
This function computes the p-value for a mixture of two continuous income distributions and a point mass for zero-incomes.
pval.md(y, dist1, dist2, theta, p0, p1, p2, dist.para.table)pval.md(y, dist1, dist2, theta, p0, p1, p2, dist.para.table)
y |
a vector with incomes. If a zero income is included, it must be the first element. |
dist1 |
character string with the name of the first continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
dist2 |
character string with the name of the second continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
theta |
vector with the parameters of dist1 and dist2. Order must be the same as in the functions for the distributions. |
p0 |
scalar with probability mass for the point mass. |
p1 |
scalar with probability mass for dist1. |
p2 |
scalar with probability mass for dist2. |
dist.para.table |
a table of the same form as |
returns the p-value.
Alexander Sohn
data(dist.para.t) ygrid<-seq(0,1e5,by=1000) theta<-c(5,1,10,3) p0<-0.2 p1<-0.3 p2<-0.5 n <-10000 y.sim <- ysample.md(n, "LOGNO", "LOGNO", theta, p0, p1, p2, dist.para.t) pval<-pval.md(ygrid,"LOGNO", "LOGNO", theta, p0, p1, p2, dist.para.table=dist.para.t) mean(y.sim<=ygrid[10]) pval[10]data(dist.para.t) ygrid<-seq(0,1e5,by=1000) theta<-c(5,1,10,3) p0<-0.2 p1<-0.3 p2<-0.5 n <-10000 y.sim <- ysample.md(n, "LOGNO", "LOGNO", theta, p0, p1, p2, dist.para.t) pval<-pval.md(ygrid,"LOGNO", "LOGNO", theta, p0, p1, p2, dist.para.table=dist.para.t) mean(y.sim<=ygrid[10]) pval[10]
This function performs a misspecificaton test for a parametrically specified cdf estimated by (Bayesian) Structured Additive Distributional Regression.
sadr.test(data, y.pos = NULL, dist1, dist2, params.m, mcmc = TRUE, mcmc.params.a, ygrid, bsrep = 10, n.startvals = 300, dist.para.table)sadr.test(data, y.pos = NULL, dist1, dist2, params.m, mcmc = TRUE, mcmc.params.a, ygrid, bsrep = 10, n.startvals = 300, dist.para.table)
data |
a dataframe including dependent variable and all explanatory variables. |
y.pos |
an integer indicating the position of the dependent variable in the dataframe. |
dist1 |
character string with the name of the first continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
dist2 |
character string with the name of the second continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
params.m |
a matrix with the estimated parameter values (in colums) for each individual (in rows). The order of the parameters must be as follows: parameters for the first distribution, parameters for the second distribution, probability of zero income, probability of dist1, probability of dist2 and probability of dist1 given employment/non-zero income. |
mcmc |
logical; if TRUE, uncertainty as provided by the MCMC samples is considered. |
mcmc.params.a |
an array, with the mcmc samples for all the parameters specified by structured additive distributional regression. In the first dimension should be the MCMC realisations, in the second dimension the individuals and in the third the parameters. The order of the parameters must be as follows: parameters for the first distribution, parameters for the second distribution, probability of zero income, probability of dist1, probability of dist2 and probability of dist1 given employment/non-zero income. |
ygrid |
vector yielding the grid on which the cdf is specified. |
bsrep |
integer giving the number of bootstrap repitions in order to determine the distributions of the test statistics under the null. |
n.startvals |
integer giving the maximum number of observations used to estimate the test statistic. |
dist.para.table |
a table of the same form as |
teststat.ks |
Kolmogorov-Smirnov test statistic. |
pval.ks |
p-value based on the Kolmogorov-Smirnov test statistic. |
teststat.cvm |
Cramer-von-Mises test statistic. |
pval.cvm |
p-value based on the Cramer-von-Mises test statistic. |
test |
type cdf considered for the test. |
param.distributions |
parametric distributions assumed for dist1 and dist2. |
teststat.ks.bs |
bootstrap results of Kolmogorov-Smirnov test statistic under null. |
teststat.cvm.bs |
bootstrap results of Cramer-von-Mises test statistic under null. |
Alexander Sohn
Rothe, C. and Wied, D. (2013): Misspecification Testing in a Class of Conditional Distributional Models, in: Journal of the American Statistical Association, Vol. 108(501), pp.314-324.
Sohn, A. (forthcoming): Scars from the Past and Future Earning Distributions.
# ### functions not run - take considerable time! # # library(acid) # data(dist.para.t) # data(params) # ### example one - two normals, no mcmc # dist1<-"norm" # dist2<-"norm" # ## generating data # set.seed(1234) # n<-1000 # sigma<-0.1 # X.theta<-c(1,10,1,10) # X.gen<-function(n,paras){ # X<-matrix(c(round(runif(n,paras[1],paras[2])),round(runif(n,paras[3], # paras[4]))),ncol=2) # return(X) # } # X <- X.gen(n,X.theta) # beta.mu1 <- 1 # beta.sigma1<- 0.1 # beta.mu2 <- 2 # beta.sigma2<- 0.1 # pi0 <- 0.3 # pi01 <- 0.8 # pi1 <- (1-pi0)*pi01 # pi2 <- 1-pi0-pi1 # # params.m<-matrix(NA,n,8) # params.m[,1]<-(0+beta.mu1)*X[,1] # params.m[,2]<-(0+beta.sigma1)*X[,1] # params.m[,3]<-(0+beta.mu2)*X[,2] # params.m[,4]<-(0+beta.sigma2)*X[,2] # params.m[,5]<-pi0 # params.m[,6]<-pi1 # params.m[,7]<-pi2 # params.m[,8]<-pi01 # # params.mF<-matrix(NA,n,8) # params.mF[,1]<-(10+beta.mu1)*X[,1] # params.mF[,2]<-(0+beta.sigma1)*X[,1] # params.mF[,3]<-(0+beta.mu2)*X[,2] # params.mF[,4]<-(2+beta.sigma2)*X[,2] # params.mF[,5]<-pi0 # params.mF[,6]<-pi1 # params.mF[,7]<-pi2 # params.mF[,8]<-pi01 # # starting repititions # reps<-30 # tsreps1T<-rep(NA,reps) # tsreps2T<-rep(NA,reps) # tsreps1F<-rep(NA,reps) # tsreps2F<-rep(NA,reps) # sys.t<-Sys.time() # for(r in 1:reps){ # Y <- rep(NA,n) # for(i in 1:n){ # Y[i] <- ysample.md(1,dist1,dist2,theta=params.m[i,1:4],params.m[i,5], # params.m[i,6],params.m[i,7],dist.para.t) # } # dat<-cbind(Y,X) # y.pos<-1 # ygrid<-seq(min(Y),round(max(Y)*1.2,-1),by=1) # tsT<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.m,mcmc=FALSE,mcmc.params=NA,ygrid=ygrid, bsrep=100, # n.startvals=30000,dist.para.table=dist.para.t) # tsreps1T[r]<-tsT$pval.ks # tsreps2T[r]<-tsT$pval.cvm # tsF<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.mF,mcmc=FALSE,mcmc.params=NA,ygrid=ygrid, bsrep=100, # n.startvals=30000,dist.para.table=dist.para.t) # tsreps1F[r]<-tsF$pval.ks # tsreps2F[r]<-tsF$pval.cvm # } # time.taken<-Sys.time()-sys.t # time.taken # cbind(tsreps1T,tsreps2T,tsreps1F,tsreps2F) # # data(dist.para.t) # data(params) # # ### example two - Dagum and log-normal - no mcmc # ##putting list elements from params into matrix form for params.m # params.m<-matrix(NA,length(params$aft.v),6+4) # params.m[,1]<-params[[which(names(params)=="bft.v")]] # params.m[,2]<-params[[which(names(params)=="aft.v")]] # params.m[,3]<-params[[which(names(params)=="cft.v")]] # params.m[,4]<-1 # params.m[,5]<-params[[which(names(params)=="mupt.v")]] # params.m[,6]<-params[[which(names(params)=="sigmapt.v")]] # params.m[,7]<-params[[which(names(params)=="punemp.v")]] # params.m[,8]<-params[[which(names(params)=="pft.v")]] # params.m[,9]<-params[[which(names(params)=="ppt.v")]] # params.m[,10]<-params[[which(names(params)=="pemp.v")]] # # set.seed(123) # reps<-30 # tsreps1T<-rep(NA,reps) # tsreps2T<-rep(NA,reps) # tsreps1F<-rep(NA,reps) # tsreps2F<-rep(NA,reps) # sys.t<-Sys.time() # for(r in 1:reps){ # ## creates variables under consideration and dimnames # n <- dim(params.m)[1] # mcmcsize<-params$mcmcsize # ages <- params$ages # unems <- params$unems # educlvls <- params$educlvls # OW <- params$OW # ## simulate two samples # ages.s <- sample(ages,n,replace=TRUE) # unems.s<- sample(unems,n,replace=TRUE) # edu.s <- sample(c(-1,1),n,replac=TRUE) # OW.s <- sample(c(-1,1),n,replac=TRUE) # y.sim<-rep(NA,n) # p.sel<-sample(1:dim(params.m)[1],n) # for(i in 1:n){ # p<-p.sel[i] # #p<-sample(1:n,1) #select a random individual # y.sim[i]<-ysample.md(1,"GB2","LOGNO", # theta=c(params$bft.v[p],params$aft.v[p], # params$cft.v[p],1, # params$mupt.v[p],params$sigmapt.v[p]), # params$punemp.v[p],params$pft.v[p],params$ppt.v[p], # dist.para.t) # } # dat<-cbind(y.sim,ages.s,unems.s,edu.s,OW.s) # y.simF<- rnorm(n,mean(y.sim),sd(y.sim)) # y.simF[y.simF<0]<-0 # datF<-dat # datF[,1]<-y.simF # ygrid <- seq(0,1e6,by=1000) #quantile(y,taus) # ##executing test # tsT<-sadr.test(data=dat,y.pos=NULL,dist1="GB2",dist2="LOGNO",params.m= # params.m[p.sel,],mcmc=FALSE,mcmc.params=NA,ygrid=ygrid, # bsrep=100,n.startvals=30000,dist.para.table=dist.para.t) # tsreps1T[r]<-tsT$pval.ks # tsreps2T[r]<-tsT$pval.cvm # tsF<-sadr.test(data=datF,y.pos=NULL,dist1="GB2",dist2="LOGNO", # params.m=params.m[p.sel,],mcmc=FALSE,mcmc.params=NA, # ygrid=ygrid, # bsrep=100,n.startvals=30000,dist.para.table=dist.para.t) # tsreps1F[r]<-tsF$pval.ks # tsreps2F[r]<-tsF$pval.cvm # } # time.taken<-Sys.time()-sys.t # time.taken # cbind(tsreps1T,tsreps2T,tsreps1F,tsreps2F) # # # # # # ### example three - two normals, with mcmc # set.seed(1234) # n<-1000 #no of observations # m<-100 #no of mcmc samples # sigma<-0.1 # X.theta<-c(1,10,1,10) # #without weights # X.gen<-function(n,paras){ # X<-matrix(c(round(runif(n,paras[1],paras[2])),round(runif(n,paras[3], # paras[4]))),ncol=2) # return(X) # } # X <- X.gen(n,X.theta) # # beta.mu1 <- 1 # beta.sigma1<- 0.1 # beta.mu2 <- 2 # beta.sigma2<- 0.1 # pi0 <- 0.3 # pi01 <- 0.8 # pi1 <- (1-pi0)*pi01 # pi2 <- 1-pi0-pi1 # # mcmc.params.a<-array(NA,dim=c(m,n,8)) # mcmc.params.a[,,1]<-(0+beta.mu1+rnorm(m,0,beta.mu1/10))%*%t(X[,1]) #assume sd of mcmc as 10% of parameter value # mcmc.params.a[,,2]<-(0+beta.sigma1+rnorm(m,0,beta.sigma1/10))%*%t(X[,1]) #must not be negative!, may be for other seed! # mcmc.params.a[,,3]<-(0+beta.mu2+rnorm(m,0,beta.mu2/10))%*%t(X[,2]) # mcmc.params.a[,,4]<-(0+beta.sigma2+rnorm(m,0,beta.sigma2/10))%*%t(X[,2]) #must not be negative!, may be for other seed! # mcmc.params.a[,,5]<-(pi0+rnorm(m,0,pi0/10))%*%t(rep(1,n)) # mcmc.params.a[,,8]<-(pi01+rnorm(m,0,pi01/10))%*%t(rep(1,n)) # mcmc.params.a[,,6]<-(1-mcmc.params.a[,,5])*mcmc.params.a[,,8] # mcmc.params.a[,,7]<-1-mcmc.params.a[,,5]-mcmc.params.a[,,6] # # params.m<-apply(mcmc.params.a,MARGIN=c(2,3),FUN=quantile,probs=0.5) # # mcmc.params.aF<-array(NA,dim=c(m,n,8)) # mcmc.params.aF[,,1]<-(10+beta.mu1+rnorm(m,0,beta.mu1/10))%*%t(X[,1]) #assume sd of mcmc as 10% of parameter value # mcmc.params.aF[,,2]<-(0+beta.sigma1+rnorm(m,0,beta.sigma1/10))%*%t(X[,1]) #must not be negative!, may be for other seed! # mcmc.params.aF[,,3]<-(0+beta.mu2+rnorm(m,0,beta.mu2/10))%*%t(X[,2]) # mcmc.params.aF[,,4]<-(2+beta.sigma2+rnorm(m,0,beta.sigma2/10))%*%t(X[,2]) #must not be negative!, may be for other seed! # mcmc.params.aF[,,5]<-(pi0+rnorm(m,0,pi0/10))%*%t(rep(1,n)) # mcmc.params.aF[,,8]<-(pi01+rnorm(m,0,pi01/10))%*%t(rep(1,n)) # mcmc.params.aF[,,6]<-(1-mcmc.params.aF[,,5])*mcmc.params.aF[,,8] # mcmc.params.aF[,,7]<-1-mcmc.params.aF[,,5]-mcmc.params.aF[,,6] # # params.mF<-apply(mcmc.params.aF,MARGIN=c(2,3),FUN=quantile,probs=0.5) # # reps<-30 # tsreps1T<-rep(NA,reps) # tsreps2T<-rep(NA,reps) # tsreps1F<-rep(NA,reps) # tsreps2F<-rep(NA,reps) # sys.t<-Sys.time() # for(r in 1:reps){ # Y <- rep(NA,n) # for(i in 1:n){ # Y[i] <- ysample.md(1,dist1,dist2,theta=params.m[i,1:4],params.m[i,5], # params.m[i,6],params.m[i,7],dist.para.t) # } # dat<-cbind(Y,X) # y.pos<-1 # ygrid<-seq(min(Y),round(max(Y)*1.2,-1),by=1) # tsT<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm",params.m= # params.m,mcmc=TRUE,mcmc.params=mcmc.params.a,ygrid=ygrid, # bsrep=100,n.startvals=30000,dist.para.table=dist.para.t) # tsreps1T[r]<-tsT$pval.ks # tsreps2T[r]<-tsT$pval.cvm # tsF<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.mF,mcmc=TRUE,mcmc.params=mcmc.params.aF, # ygrid=ygrid, bsrep=100,n.startvals=30000, # dist.para.table=dist.para.t) # tsreps1F[r]<-tsF$pval.ks # tsreps2F[r]<-tsF$pval.cvm # #c(ts$teststat.ks,ts$teststat.cvm) # #c(ts$pval.ks,ts$pval.cvm) # # } # time.taken<-Sys.time()-sys.t # time.taken # cbind(tsreps1T,tsreps2T,tsreps1F,tsreps2F) # # # # ### example four - two normals, with mcmc and slight deviation from truth # in true params # library(acid) # data(dist.para.t) # data(params) # dist1<-"norm" # dist2<-"norm" # # set.seed(1234) # n<-1000 #no of observations # m<-100 #no of mcmc samples # sigma<-0.1 # X.theta<-c(1,10,1,10) # #without weights # X.gen<-function(n,paras){ # X<-matrix(c(round(runif(n,paras[1],paras[2])),round(runif(n,paras[3], # paras[4]))),ncol=2) # return(X) # } # X <- X.gen(n,X.theta) # # beta.mu1 <- 1 # beta.sigma1<- 0.1 # beta.mu2 <- 2 # beta.sigma2<- 0.1 # pi0 <- 0.3 # pi01 <- 0.8 # pi1 <- (1-pi0)*pi01 # pi2 <- 1-pi0-pi1 # # mcmc.params.a<-array(NA,dim=c(m,n,8)) # mcmc.params.a[,,1]<-(beta.mu1/10+beta.mu1+rnorm(m,0,beta.mu1/10))%*%t(X[,1]) #assume sd of mcmc as 10% of parameter value # mcmc.params.a[,,2]<-(0+beta.sigma1+rnorm(m,0,beta.sigma1/10))%*%t(X[,1]) #must not be negative!, may be for other seed! # mcmc.params.a[,,3]<-(0+beta.mu2+rnorm(m,0,beta.mu2/10))%*%t(X[,2]) # mcmc.params.a[,,4]<-(beta.sigma2/10+beta.sigma2+rnorm(m,0, # beta.sigma2/10))%*%t(X[,2]) #must not be negative!, may be for other seed! # mcmc.params.a[,,5]<-(pi0+rnorm(m,0,pi0/10))%*%t(rep(1,n)) # mcmc.params.a[,,8]<-(pi01+rnorm(m,0,pi01/10))%*%t(rep(1,n)) # mcmc.params.a[,,6]<-(1-mcmc.params.a[,,5])*mcmc.params.a[,,8] # mcmc.params.a[,,7]<-1-mcmc.params.a[,,5]-mcmc.params.a[,,6] # # params.m<-apply(mcmc.params.a,MARGIN=c(2,3),FUN=quantile,probs=0.5) # # mcmc.params.aF<-array(NA,dim=c(m,n,8)) # mcmc.params.aF[,,1]<-(10+beta.mu1+rnorm(m,0,beta.mu1/10))%*%t(X[,1]) #assume sd of mcmc as 10% of parameter value # mcmc.params.aF[,,2]<-(0+beta.sigma1+rnorm(m,0,beta.sigma1/10))%*%t(X[,1]) #must not be negative!, may be for other seed! # mcmc.params.aF[,,3]<-(0+beta.mu2+rnorm(m,0,beta.mu2/10))%*%t(X[,2]) # mcmc.params.aF[,,4]<-(2+beta.sigma2+rnorm(m,0,beta.sigma2/10))%*%t(X[,2]) #must not be negative!, may be for other seed! # mcmc.params.aF[,,5]<-(pi0+rnorm(m,0,pi0/10))%*%t(rep(1,n)) # mcmc.params.aF[,,8]<-(pi01+rnorm(m,0,pi01/10))%*%t(rep(1,n)) # mcmc.params.aF[,,6]<-(1-mcmc.params.aF[,,5])*mcmc.params.aF[,,8] # mcmc.params.aF[,,7]<-1-mcmc.params.aF[,,5]-mcmc.params.aF[,,6] # # params.mF<-apply(mcmc.params.aF,MARGIN=c(2,3),FUN=quantile,probs=0.5) # # reps<-30 # tsreps1T<-rep(NA,reps) # tsreps2T<-rep(NA,reps) # tsreps1F<-rep(NA,reps) # tsreps2F<-rep(NA,reps) # sys.t<-Sys.time() # for(r in 1:reps){ # Y <- rep(NA,n) # for(i in 1:n){ # Y[i] <- ysample.md(1,dist1,dist2,theta=params.m[i,1:4],params.m[i,5], # params.m[i,6],params.m[i,7],dist.para.t) # } # # dat<-cbind(Y,X) # y.pos<-1 # ygrid<-seq(min(Y),round(max(Y)*1.2,-1),by=1) # tsT<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.m,mcmc=TRUE,mcmc.params=mcmc.params.a, # ygrid=ygrid, bsrep=100,n.startvals=30000, # dist.para.table=dist.para.t) # tsreps1T[r]<-tsT$pval.ks # tsreps2T[r]<-tsT$pval.cvm # tsF<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.mF,mcmc=TRUE,mcmc.params=mcmc.params.aF, # ygrid=ygrid, bsrep=100,n.startvals=30000, # dist.para.table=dist.para.t) # tsreps1F[r]<-tsF$pval.ks # tsreps2F[r]<-tsF$pval.cvm # #c(ts$teststat.ks,ts$teststat.cvm) # #c(ts$pval.ks,ts$pval.cvm) # # } # time.taken<-Sys.time()-sys.t # time.taken # cbind(tsreps1T,tsreps2T,tsreps1F,tsreps2F)# ### functions not run - take considerable time! # # library(acid) # data(dist.para.t) # data(params) # ### example one - two normals, no mcmc # dist1<-"norm" # dist2<-"norm" # ## generating data # set.seed(1234) # n<-1000 # sigma<-0.1 # X.theta<-c(1,10,1,10) # X.gen<-function(n,paras){ # X<-matrix(c(round(runif(n,paras[1],paras[2])),round(runif(n,paras[3], # paras[4]))),ncol=2) # return(X) # } # X <- X.gen(n,X.theta) # beta.mu1 <- 1 # beta.sigma1<- 0.1 # beta.mu2 <- 2 # beta.sigma2<- 0.1 # pi0 <- 0.3 # pi01 <- 0.8 # pi1 <- (1-pi0)*pi01 # pi2 <- 1-pi0-pi1 # # params.m<-matrix(NA,n,8) # params.m[,1]<-(0+beta.mu1)*X[,1] # params.m[,2]<-(0+beta.sigma1)*X[,1] # params.m[,3]<-(0+beta.mu2)*X[,2] # params.m[,4]<-(0+beta.sigma2)*X[,2] # params.m[,5]<-pi0 # params.m[,6]<-pi1 # params.m[,7]<-pi2 # params.m[,8]<-pi01 # # params.mF<-matrix(NA,n,8) # params.mF[,1]<-(10+beta.mu1)*X[,1] # params.mF[,2]<-(0+beta.sigma1)*X[,1] # params.mF[,3]<-(0+beta.mu2)*X[,2] # params.mF[,4]<-(2+beta.sigma2)*X[,2] # params.mF[,5]<-pi0 # params.mF[,6]<-pi1 # params.mF[,7]<-pi2 # params.mF[,8]<-pi01 # # starting repititions # reps<-30 # tsreps1T<-rep(NA,reps) # tsreps2T<-rep(NA,reps) # tsreps1F<-rep(NA,reps) # tsreps2F<-rep(NA,reps) # sys.t<-Sys.time() # for(r in 1:reps){ # Y <- rep(NA,n) # for(i in 1:n){ # Y[i] <- ysample.md(1,dist1,dist2,theta=params.m[i,1:4],params.m[i,5], # params.m[i,6],params.m[i,7],dist.para.t) # } # dat<-cbind(Y,X) # y.pos<-1 # ygrid<-seq(min(Y),round(max(Y)*1.2,-1),by=1) # tsT<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.m,mcmc=FALSE,mcmc.params=NA,ygrid=ygrid, bsrep=100, # n.startvals=30000,dist.para.table=dist.para.t) # tsreps1T[r]<-tsT$pval.ks # tsreps2T[r]<-tsT$pval.cvm # tsF<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.mF,mcmc=FALSE,mcmc.params=NA,ygrid=ygrid, bsrep=100, # n.startvals=30000,dist.para.table=dist.para.t) # tsreps1F[r]<-tsF$pval.ks # tsreps2F[r]<-tsF$pval.cvm # } # time.taken<-Sys.time()-sys.t # time.taken # cbind(tsreps1T,tsreps2T,tsreps1F,tsreps2F) # # data(dist.para.t) # data(params) # # ### example two - Dagum and log-normal - no mcmc # ##putting list elements from params into matrix form for params.m # params.m<-matrix(NA,length(params$aft.v),6+4) # params.m[,1]<-params[[which(names(params)=="bft.v")]] # params.m[,2]<-params[[which(names(params)=="aft.v")]] # params.m[,3]<-params[[which(names(params)=="cft.v")]] # params.m[,4]<-1 # params.m[,5]<-params[[which(names(params)=="mupt.v")]] # params.m[,6]<-params[[which(names(params)=="sigmapt.v")]] # params.m[,7]<-params[[which(names(params)=="punemp.v")]] # params.m[,8]<-params[[which(names(params)=="pft.v")]] # params.m[,9]<-params[[which(names(params)=="ppt.v")]] # params.m[,10]<-params[[which(names(params)=="pemp.v")]] # # set.seed(123) # reps<-30 # tsreps1T<-rep(NA,reps) # tsreps2T<-rep(NA,reps) # tsreps1F<-rep(NA,reps) # tsreps2F<-rep(NA,reps) # sys.t<-Sys.time() # for(r in 1:reps){ # ## creates variables under consideration and dimnames # n <- dim(params.m)[1] # mcmcsize<-params$mcmcsize # ages <- params$ages # unems <- params$unems # educlvls <- params$educlvls # OW <- params$OW # ## simulate two samples # ages.s <- sample(ages,n,replace=TRUE) # unems.s<- sample(unems,n,replace=TRUE) # edu.s <- sample(c(-1,1),n,replac=TRUE) # OW.s <- sample(c(-1,1),n,replac=TRUE) # y.sim<-rep(NA,n) # p.sel<-sample(1:dim(params.m)[1],n) # for(i in 1:n){ # p<-p.sel[i] # #p<-sample(1:n,1) #select a random individual # y.sim[i]<-ysample.md(1,"GB2","LOGNO", # theta=c(params$bft.v[p],params$aft.v[p], # params$cft.v[p],1, # params$mupt.v[p],params$sigmapt.v[p]), # params$punemp.v[p],params$pft.v[p],params$ppt.v[p], # dist.para.t) # } # dat<-cbind(y.sim,ages.s,unems.s,edu.s,OW.s) # y.simF<- rnorm(n,mean(y.sim),sd(y.sim)) # y.simF[y.simF<0]<-0 # datF<-dat # datF[,1]<-y.simF # ygrid <- seq(0,1e6,by=1000) #quantile(y,taus) # ##executing test # tsT<-sadr.test(data=dat,y.pos=NULL,dist1="GB2",dist2="LOGNO",params.m= # params.m[p.sel,],mcmc=FALSE,mcmc.params=NA,ygrid=ygrid, # bsrep=100,n.startvals=30000,dist.para.table=dist.para.t) # tsreps1T[r]<-tsT$pval.ks # tsreps2T[r]<-tsT$pval.cvm # tsF<-sadr.test(data=datF,y.pos=NULL,dist1="GB2",dist2="LOGNO", # params.m=params.m[p.sel,],mcmc=FALSE,mcmc.params=NA, # ygrid=ygrid, # bsrep=100,n.startvals=30000,dist.para.table=dist.para.t) # tsreps1F[r]<-tsF$pval.ks # tsreps2F[r]<-tsF$pval.cvm # } # time.taken<-Sys.time()-sys.t # time.taken # cbind(tsreps1T,tsreps2T,tsreps1F,tsreps2F) # # # # # # ### example three - two normals, with mcmc # set.seed(1234) # n<-1000 #no of observations # m<-100 #no of mcmc samples # sigma<-0.1 # X.theta<-c(1,10,1,10) # #without weights # X.gen<-function(n,paras){ # X<-matrix(c(round(runif(n,paras[1],paras[2])),round(runif(n,paras[3], # paras[4]))),ncol=2) # return(X) # } # X <- X.gen(n,X.theta) # # beta.mu1 <- 1 # beta.sigma1<- 0.1 # beta.mu2 <- 2 # beta.sigma2<- 0.1 # pi0 <- 0.3 # pi01 <- 0.8 # pi1 <- (1-pi0)*pi01 # pi2 <- 1-pi0-pi1 # # mcmc.params.a<-array(NA,dim=c(m,n,8)) # mcmc.params.a[,,1]<-(0+beta.mu1+rnorm(m,0,beta.mu1/10))%*%t(X[,1]) #assume sd of mcmc as 10% of parameter value # mcmc.params.a[,,2]<-(0+beta.sigma1+rnorm(m,0,beta.sigma1/10))%*%t(X[,1]) #must not be negative!, may be for other seed! # mcmc.params.a[,,3]<-(0+beta.mu2+rnorm(m,0,beta.mu2/10))%*%t(X[,2]) # mcmc.params.a[,,4]<-(0+beta.sigma2+rnorm(m,0,beta.sigma2/10))%*%t(X[,2]) #must not be negative!, may be for other seed! # mcmc.params.a[,,5]<-(pi0+rnorm(m,0,pi0/10))%*%t(rep(1,n)) # mcmc.params.a[,,8]<-(pi01+rnorm(m,0,pi01/10))%*%t(rep(1,n)) # mcmc.params.a[,,6]<-(1-mcmc.params.a[,,5])*mcmc.params.a[,,8] # mcmc.params.a[,,7]<-1-mcmc.params.a[,,5]-mcmc.params.a[,,6] # # params.m<-apply(mcmc.params.a,MARGIN=c(2,3),FUN=quantile,probs=0.5) # # mcmc.params.aF<-array(NA,dim=c(m,n,8)) # mcmc.params.aF[,,1]<-(10+beta.mu1+rnorm(m,0,beta.mu1/10))%*%t(X[,1]) #assume sd of mcmc as 10% of parameter value # mcmc.params.aF[,,2]<-(0+beta.sigma1+rnorm(m,0,beta.sigma1/10))%*%t(X[,1]) #must not be negative!, may be for other seed! # mcmc.params.aF[,,3]<-(0+beta.mu2+rnorm(m,0,beta.mu2/10))%*%t(X[,2]) # mcmc.params.aF[,,4]<-(2+beta.sigma2+rnorm(m,0,beta.sigma2/10))%*%t(X[,2]) #must not be negative!, may be for other seed! # mcmc.params.aF[,,5]<-(pi0+rnorm(m,0,pi0/10))%*%t(rep(1,n)) # mcmc.params.aF[,,8]<-(pi01+rnorm(m,0,pi01/10))%*%t(rep(1,n)) # mcmc.params.aF[,,6]<-(1-mcmc.params.aF[,,5])*mcmc.params.aF[,,8] # mcmc.params.aF[,,7]<-1-mcmc.params.aF[,,5]-mcmc.params.aF[,,6] # # params.mF<-apply(mcmc.params.aF,MARGIN=c(2,3),FUN=quantile,probs=0.5) # # reps<-30 # tsreps1T<-rep(NA,reps) # tsreps2T<-rep(NA,reps) # tsreps1F<-rep(NA,reps) # tsreps2F<-rep(NA,reps) # sys.t<-Sys.time() # for(r in 1:reps){ # Y <- rep(NA,n) # for(i in 1:n){ # Y[i] <- ysample.md(1,dist1,dist2,theta=params.m[i,1:4],params.m[i,5], # params.m[i,6],params.m[i,7],dist.para.t) # } # dat<-cbind(Y,X) # y.pos<-1 # ygrid<-seq(min(Y),round(max(Y)*1.2,-1),by=1) # tsT<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm",params.m= # params.m,mcmc=TRUE,mcmc.params=mcmc.params.a,ygrid=ygrid, # bsrep=100,n.startvals=30000,dist.para.table=dist.para.t) # tsreps1T[r]<-tsT$pval.ks # tsreps2T[r]<-tsT$pval.cvm # tsF<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.mF,mcmc=TRUE,mcmc.params=mcmc.params.aF, # ygrid=ygrid, bsrep=100,n.startvals=30000, # dist.para.table=dist.para.t) # tsreps1F[r]<-tsF$pval.ks # tsreps2F[r]<-tsF$pval.cvm # #c(ts$teststat.ks,ts$teststat.cvm) # #c(ts$pval.ks,ts$pval.cvm) # # } # time.taken<-Sys.time()-sys.t # time.taken # cbind(tsreps1T,tsreps2T,tsreps1F,tsreps2F) # # # # ### example four - two normals, with mcmc and slight deviation from truth # in true params # library(acid) # data(dist.para.t) # data(params) # dist1<-"norm" # dist2<-"norm" # # set.seed(1234) # n<-1000 #no of observations # m<-100 #no of mcmc samples # sigma<-0.1 # X.theta<-c(1,10,1,10) # #without weights # X.gen<-function(n,paras){ # X<-matrix(c(round(runif(n,paras[1],paras[2])),round(runif(n,paras[3], # paras[4]))),ncol=2) # return(X) # } # X <- X.gen(n,X.theta) # # beta.mu1 <- 1 # beta.sigma1<- 0.1 # beta.mu2 <- 2 # beta.sigma2<- 0.1 # pi0 <- 0.3 # pi01 <- 0.8 # pi1 <- (1-pi0)*pi01 # pi2 <- 1-pi0-pi1 # # mcmc.params.a<-array(NA,dim=c(m,n,8)) # mcmc.params.a[,,1]<-(beta.mu1/10+beta.mu1+rnorm(m,0,beta.mu1/10))%*%t(X[,1]) #assume sd of mcmc as 10% of parameter value # mcmc.params.a[,,2]<-(0+beta.sigma1+rnorm(m,0,beta.sigma1/10))%*%t(X[,1]) #must not be negative!, may be for other seed! # mcmc.params.a[,,3]<-(0+beta.mu2+rnorm(m,0,beta.mu2/10))%*%t(X[,2]) # mcmc.params.a[,,4]<-(beta.sigma2/10+beta.sigma2+rnorm(m,0, # beta.sigma2/10))%*%t(X[,2]) #must not be negative!, may be for other seed! # mcmc.params.a[,,5]<-(pi0+rnorm(m,0,pi0/10))%*%t(rep(1,n)) # mcmc.params.a[,,8]<-(pi01+rnorm(m,0,pi01/10))%*%t(rep(1,n)) # mcmc.params.a[,,6]<-(1-mcmc.params.a[,,5])*mcmc.params.a[,,8] # mcmc.params.a[,,7]<-1-mcmc.params.a[,,5]-mcmc.params.a[,,6] # # params.m<-apply(mcmc.params.a,MARGIN=c(2,3),FUN=quantile,probs=0.5) # # mcmc.params.aF<-array(NA,dim=c(m,n,8)) # mcmc.params.aF[,,1]<-(10+beta.mu1+rnorm(m,0,beta.mu1/10))%*%t(X[,1]) #assume sd of mcmc as 10% of parameter value # mcmc.params.aF[,,2]<-(0+beta.sigma1+rnorm(m,0,beta.sigma1/10))%*%t(X[,1]) #must not be negative!, may be for other seed! # mcmc.params.aF[,,3]<-(0+beta.mu2+rnorm(m,0,beta.mu2/10))%*%t(X[,2]) # mcmc.params.aF[,,4]<-(2+beta.sigma2+rnorm(m,0,beta.sigma2/10))%*%t(X[,2]) #must not be negative!, may be for other seed! # mcmc.params.aF[,,5]<-(pi0+rnorm(m,0,pi0/10))%*%t(rep(1,n)) # mcmc.params.aF[,,8]<-(pi01+rnorm(m,0,pi01/10))%*%t(rep(1,n)) # mcmc.params.aF[,,6]<-(1-mcmc.params.aF[,,5])*mcmc.params.aF[,,8] # mcmc.params.aF[,,7]<-1-mcmc.params.aF[,,5]-mcmc.params.aF[,,6] # # params.mF<-apply(mcmc.params.aF,MARGIN=c(2,3),FUN=quantile,probs=0.5) # # reps<-30 # tsreps1T<-rep(NA,reps) # tsreps2T<-rep(NA,reps) # tsreps1F<-rep(NA,reps) # tsreps2F<-rep(NA,reps) # sys.t<-Sys.time() # for(r in 1:reps){ # Y <- rep(NA,n) # for(i in 1:n){ # Y[i] <- ysample.md(1,dist1,dist2,theta=params.m[i,1:4],params.m[i,5], # params.m[i,6],params.m[i,7],dist.para.t) # } # # dat<-cbind(Y,X) # y.pos<-1 # ygrid<-seq(min(Y),round(max(Y)*1.2,-1),by=1) # tsT<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.m,mcmc=TRUE,mcmc.params=mcmc.params.a, # ygrid=ygrid, bsrep=100,n.startvals=30000, # dist.para.table=dist.para.t) # tsreps1T[r]<-tsT$pval.ks # tsreps2T[r]<-tsT$pval.cvm # tsF<-sadr.test(data=dat,y.pos=NULL,dist1="norm",dist2="norm", # params.m=params.mF,mcmc=TRUE,mcmc.params=mcmc.params.aF, # ygrid=ygrid, bsrep=100,n.startvals=30000, # dist.para.table=dist.para.t) # tsreps1F[r]<-tsF$pval.ks # tsreps2F[r]<-tsF$pval.cvm # #c(ts$teststat.ks,ts$teststat.cvm) # #c(ts$pval.ks,ts$pval.cvm) # # } # time.taken<-Sys.time()-sys.t # time.taken # cbind(tsreps1T,tsreps2T,tsreps1F,tsreps2F)
This function calculates the standard deviation of the Generalised Beta Distribution of Second Kind.
sd.GB2(b, a, p, q)sd.GB2(b, a, p, q)
b |
the parameter b of the Dagum distribution as defined by Kleiber and Kotz (2003). |
a |
the parameter a of the Dagum distribution as defined by Kleiber and Kotz (2003). |
p |
the parameter p of the Dagum distribution as defined by Kleiber and Kotz (2003). |
q |
the parameter q of the Dagum distribution as defined by Kleiber and Kotz (2003). |
returns the standard deviation.
Alexander Sohn
Kleiber, C. and Kotz, S. (2003): Statistical Size Distributions in Economics and Actuarial Sciences, Wiley, Hoboken.
a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,q.test) sd.GB2(b.test,a.test,p.test,q.test) sd(GB2sample)a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,q.test) sd.GB2(b.test,a.test,p.test,q.test) sd(GB2sample)
This function computes the Single-parameter Gini coefficient (a.k.a. generalised Gini coefficient or extended Gini coefficient) for a vector of observations.
sgini(x, nu = 2, w = NULL)sgini(x, nu = 2, w = NULL)
x |
a vector of observations. |
nu |
a scalar entailing the parameter that tunes the degree of the policy maker's aversion to inequality. See Yaari, 1988 for details. |
w |
a vector of weights. |
Gini |
the Gini coefficient for the sample. |
bcGini |
the bias-corrected Gini coefficient for the sample. |
Alexander Sohn
van Kerm, P. (2009): sgini - Generalized Gini and Concentration coefficients (with factor decomposition) in Stata', CEPS/INSTEAD, Differdange, Luxembourg.
Yaari, M.E. (1988): A Controversal Proposal Concerning Inequality Measurement, Journal of Economic Theory, Vol. 44, pp. 381-397.
set.seed(123) x <- rnorm(100,10,1) gini(x)$Gini sgini(x,nu=2)$Giniset.seed(123) x <- rnorm(100,10,1) gini(x)$Gini sgini(x,nu=2)$Gini
This function approximates the Single-parameter Gini coefficient for a distribution specified by a vector of densities and a corresponding income vector. A point mass at zero is allowed.
sgini.den(incs, dens, nu = 2, pm0 = NA, lower = NULL, upper = NULL)sgini.den(incs, dens, nu = 2, pm0 = NA, lower = NULL, upper = NULL)
incs |
a vector with sorted income values. |
dens |
a vector with the corresponding densities. |
nu |
a scalar entailing the parameter that tunes the degree of the policy maker's aversion to inequality. See Yaari, 1988 for details. |
pm0 |
the point mass for zero incomes. If not specified no point mass is assumed. |
lower |
the lower bound of the income range considered. |
upper |
the upper bound of the income range considered. |
Gini |
the approximation of the Gini coefficient. |
pm0 |
the point mass for zero incomes used. |
lower |
the lower bound of the income range considered used. |
upper |
the upper bound of the income range considered used. |
Alexander Sohn
van Kerm, P. (2009): sgini - Generalized Gini and Concentration coefficients (with factor decomposition) in Stata', CEPS/INSTEAD, Differdange, Luxembourg.
Yaari, M.E. (1988): A Controversal Proposal Concerning Inequality Measurement, Journal of Economic Theory, Vol. 44, pp. 381-397.
## without point mass set.seed(123) x <- rnorm(1000,10,1) incs <- seq(1,20,length.out=1000) dens <- dnorm(incs,10,1) lower=NULL;upper=NULL;pm0<-NA gini(x)$Gini sgini(x,nu=2)$Gini sgini.den(incs,dens)$Gini ## with point mass set.seed(123) x <- c(rep(0,1000),rnorm(1000,10,1)) incs <- c(0,seq(1,20,length.out=1000)) dens <- c(0.5,dnorm(incs[-1],10,1)/2) gini(x)$Gini sgini(x,nu=2)$Gini sgini.den(incs,dens,pm = 0.5)$Gini## without point mass set.seed(123) x <- rnorm(1000,10,1) incs <- seq(1,20,length.out=1000) dens <- dnorm(incs,10,1) lower=NULL;upper=NULL;pm0<-NA gini(x)$Gini sgini(x,nu=2)$Gini sgini.den(incs,dens)$Gini ## with point mass set.seed(123) x <- c(rep(0,1000),rnorm(1000,10,1)) incs <- c(0,seq(1,20,length.out=1000)) dens <- c(0.5,dnorm(incs[-1],10,1)/2) gini(x)$Gini sgini(x,nu=2)$Gini sgini.den(incs,dens,pm = 0.5)$Gini
This function calculates the skewness of the Generalised Beta Distribution of Second Kind.
skewness.GB2(b, a, p, q)skewness.GB2(b, a, p, q)
b |
the parameter b of the Dagum distribution as defined by Kleiber and Kotz (2003). |
a |
the parameter a of the Dagum distribution as defined by Kleiber and Kotz (2003). |
p |
the parameter p of the Dagum distribution as defined by Kleiber and Kotz (2003). |
q |
the parameter q of the Dagum distribution as defined by Kleiber and Kotz (2003). |
returns the skewness.
Alexander Sohn
Kleiber, C. and Kotz, S. (2003): Statistical Size Distributions in Economics and Actuarial Sciences, Wiley, Hoboken.
a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,q.test) skewness.GB2(b.test,a.test,p.test,q.test) #require(e1071) #skewness(GB2sample)#note that this estimation is highly unstable even for larger sample sizes.a.test<- 4 b.test<- 20000 p.test<- 0.7 q.test<- 1 alpha.test<-1 GB2sample<-rGB2(10000,b.test,a.test,p.test,q.test) skewness.GB2(b.test,a.test,p.test,q.test) #require(e1071) #skewness(GB2sample)#note that this estimation is highly unstable even for larger sample sizes.
This function computes the Theil index for the gamma distribution.
theil.gamma(p)theil.gamma(p)
p |
the shape parameter p of the gamma distribution as defined by Kleiber and Kotz (2003). |
returns the Theil index.
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 1-86, Elsevier, Amsterdam.
Kleiber, C. and Kotz, S. (2003): Statistical Size Distributions in Economics and Actuarial Sciences, Wiley, Hoboken.
shape.test <- 5 scale.test <- 50000 y <- rgamma(10000,shape=shape.test,scale=scale.test) entropy(y,1) theil.gamma(shape.test)shape.test <- 5 scale.test <- 50000 y <- rgamma(10000,shape=shape.test,scale=scale.test) entropy(y,1) theil.gamma(shape.test)
This function computes the Atkinson inequality index for a vector of observations with corresponding weights.
weighted.atkinson(x, w = NULL, epsilon = 1, wscale = 1000)weighted.atkinson(x, w = NULL, epsilon = 1, wscale = 1000)
x |
a vector of observations. |
w |
a vector of weights. If |
epsilon |
inequality aversion parameter as denoted by Atkinson (1970). The default is epsilon=1. |
wscale |
a scale by which the weights are adjusted such that can be rounded to natural numbers. |
returns the selected Atkinson inequality index.
Alexander Sohn
Atkinson, A.B. (1970): On the Measurment of Inequality, in: Journal of Economic Theory, Vol. 2(3), pp. 244-263.
x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) w <- sample(1:2,length(x),replace=TRUE) weighted.atkinson(x,w)x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) w <- sample(1:2,length(x),replace=TRUE) weighted.atkinson(x,w)
This function computes the Coefficient of Variation for a vector of observations and corresponding weights.
weighted.coeffvar(x, w)weighted.coeffvar(x, w)
x |
a vector of observations. |
w |
a vector of weights. |
cv |
returns the coefficient of variation without bias correction. |
bccv |
returns the coefficient of variation with bias correction. |
Weighting is not properly accounted for in the sample adjustment of bccv!
Alexander Sohn
Atkinson, A.B. and Bourguignon, F. (2000): Income Distribution and Economics, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 1-86, Elsevier, Amsterdam.
# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) w <- sample(1:10,length(x), replace=TRUE) weighted.coeffvar(x,w)# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) w <- sample(1:10,length(x), replace=TRUE) weighted.coeffvar(x,w)
This function computes the Measures of the Generalised Entropy Family for a vector of observations with corresponding weights.
weighted.entropy(x, w = NULL, alpha = 1)weighted.entropy(x, w = NULL, alpha = 1)
x |
a vector of observations. |
w |
a vector of weights. |
alpha |
the parameter for the generalised entropy family of measures, denoted by alpha by Cowell (2000). Note that this parameter notation differs from the notation used in the ineq package. |
returns the entropy measure.
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 1-86, Elsevier, Amsterdam.
# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) w <- sample(1:2,length(x),replace=TRUE) weighted.entropy(x,w)# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) w <- sample(1:2,length(x),replace=TRUE) weighted.entropy(x,w)
This function computes the Gini coefficient for a vector of observations with corresponding weights.
weighted.gini(x, w = NULL)weighted.gini(x, w = NULL)
x |
a vector of observations. |
w |
a vector of weights. |
returns the Gini coefficient.
Alexander Sohn
Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 1-86, Elsevier, Amsterdam.
# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) w <- sample(1:2,length(x),replace=TRUE) weighted.gini(x,w)# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) w <- sample(1:2,length(x),replace=TRUE) weighted.gini(x,w)
This functions calculates the first three moments as well as mean, standard deviation and skewness for a vector of observations with corresponding weights.
weighted.moments(x, w8 = NULL)weighted.moments(x, w8 = NULL)
x |
a vector of observations. |
w8 |
a vector of weights. |
fm |
returns the first moment. |
weighted.mean |
returns the mean. |
sm |
returns the second moment. |
weighted.sd |
returns the uncorrected (population) standard deviation. |
wtd.sd |
returns the sample-size corrected standard deviation estimate. |
tm |
returns the third moment. |
w.skew.SAS |
returns the skewness estimate as implemented in SAS. |
w.skew.Stata |
returns the skewness estimate as implemented in Stata. |
Alexander Sohn
This function samples incomes from a mixture of two continuous income distributions and a point mass for zero-incomes.
ysample.md(n, dist1, dist2, theta, p0, p1, p2, dist.para.table)ysample.md(n, dist1, dist2, theta, p0, p1, p2, dist.para.table)
n |
number of observations. |
dist1 |
character string with the name of the first continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
dist2 |
character string with the name of the second continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution. |
theta |
vector with the parameters of dist1 and dist2. Order must be the same as in the functions for the distributions. |
p0 |
scalar with probability mass for the point mass. |
p1 |
scalar with probability mass for dist1. |
p2 |
scalar with probability mass for dist2. |
dist.para.table |
a table of the same form as |
returns the sample of observations.
Alexander Sohn
data(dist.para.t) ygrid<-seq(0,1e5,by=1000) theta<-c(5,1,10,3) p0<-0.2 p1<-0.3 p2<-0.5 n <-10 ysample.md(n, "LOGNO", "LOGNO", theta, p0, p1, p2, dist.para.t)data(dist.para.t) ygrid<-seq(0,1e5,by=1000) theta<-c(5,1,10,3) p0<-0.2 p1<-0.3 p2<-0.5 n <-10 ysample.md(n, "LOGNO", "LOGNO", theta, p0, p1, p2, dist.para.t)